Tampere University has several professor positions open related to AI and its applications, covering various areas of signal processing. The positions include a quite substantial starting package, covering funding for multiple research group members. Strong researchers are encouraged to apply! The deadline for applications is 9 March 2025. For more information about the positions, please visit this page.
Context
Coastal radars aim to control and monitor the maritime surface. By matching transmitted and received electromagnetic waves, radar is able to detect boats whose back-scattered a sufficiently strong signal relative to the sea clutter. The detection performance generally depends on the target’s signature and the clutter noise power, which can be summarized by the Signal to Noise Ratio (SNR). In a rough sea state (e.g., 5 on the Douglas sea scale), the detection performance of small boats hidden by strong sea clutter (Bragg clutter) can deteriorate drastically. The postdoc aims to innovate and improve theoretical detection methods previously developed in SONDRA and L2S laboratories in this context.
Research
The postdoc will first investigate robust detection methods such as Adaptive Normalized Matched Filters (ANMF) [1], which require estimating the covariance matrix of secondary data in a robust manner [2]. The covariance matrix estimation step could include prior information on the structure using either Riemannian geometry [3] or optimization under persymmetric [4], Toeplitz [5], Kronecker constraints [6], etc. This step could be crucial for improving detection and mitigating the probability of false alarms. The second direction investigates deep learning approaches to handle a detection, segmentation or/and generation scheme, either end-to-end or in an unrolling way [7, 8]. The latter approach can be less data-hungry and easier to interpret. Since the available data are complex-valued, an architecture based on Complex-Valued Neural Networks (CVNN) [9] can be exploited to learn data phase information. Meta-learning methods can be investigated to improve detection performance. The developed algorithms will be tested on CSIR database.
Requirements
This position is funded by ANR NEPTUNE 3. We seek a highly motivated postdoctoral fellow to investigate statistical and deep learning methods for detection. The ideal candidate should possess the following qualifications:
• A robust background in machine learning, signal processing, or applied mathematics (statistics, optimization, etc.).
• Strong programming abilities in either Matlab or Python
Supervision team contacts:
Jean-Philippe Ovarlez, SONDRA, CentraleSupelec, jeanphilippe.ovarlez@centralesupelec.fr
Frédéric Pascal, L2S, CentraleSupelec, frederic.pascal@centralesupelec.fr
Mohammed Nabil El Korso, L2S, CentraleSupelec, mohammed.el-korso@universite-paris-saclay.fr
Chengfang Ren, SONDRA, CentraleSupelec, chengfang.ren@centralesupelec.fr
References
[1] J.-P. Ovarlez, F. Pascal, and A. Breloy, “Asymptotic detection performance analysis of the robust adaptive normalized matched filter,” in 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 137–140, 2015.
[2] F. Pascal, Y. Chitour, J.-P. Ovarlez, P. Forster, and P. Larzabal, “Covariance structure maximum-likelihood estimates in compound gaussian noise: Existence and algorithm analysis,” IEEE Transactions on Signal Processing, vol. 56, no. 1, pp. 34–48, 2008.
[3] A. Collas, Geometrie riemannienne pour l’estimation et l’apprentissage statistiques : application a la teledetection. Theses, Universit´e Paris-Saclay, Nov. 2022.
[4] G. Pailloux, P. Forster, J.-P. Ovarlez, and F. Pascal, “Persymmetric adaptive radar detectors,” IEEE Transactions on Aerospace and Electronic Systems, vol. 47, no. 4, pp. 2376–2390, 2011.
[5] B. Meriaux, C. Ren, M. N. El Korso, A. Breloy, and P. Forster, “Robust-comet for covariance estimation in convex structures: Algorithm and statistical properties,” in 2017 IEEE 7th Inter national Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 1–5, 2017.
[6] B. M´eriaux, C. Ren, A. Breloy, M. N. E. Korso, and P. Forster, “Matched and mismatched estimation of kronecker product of linearly structured scatter matrices under elliptical distributions,” IEEE Transactions on Signal Processing, vol. 69, pp. 603–616, 2021.
[7] V. Monga, Y. Li, and Y. C. Eldar, “Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing,” IEEE Signal Processing Magazine, vol. 38, no. 2, pp. 18–44, 2021.
[8] N. Arab, Y. Mhiri, I. Vin, M. N. El Korso and P. Larzabal "Unrolled Expectation Maximization for Sparse Radio Interferometric Imaging", EUSIPCO, 2024.
[9] J. A. Barrachina, Complex-valued neural networks for radar applications. Theses, Universite Paris-Saclay, Dec. 2022.
The Delft Center for Systems and Control (DCSC) is offering one PhD vacancy under the newly funded ‘Digitally modulated radars’ (DiMoRA) project. The project will be carried out in the research group of Dr. N. J. Myers, in collaboration with researchers at the faculty of EEMCS and experts from NXP Semiconductors.
Dr. Myers’s group at TU Delft develops and analyzes novel signal processing techniques for communications and sensing with wireless systems. We focus on both applied and theoretical aspects of challenging problems in connected automotive and radars. For more details on the vacancy and the application process, visit this link.
The University of Texas at El Paso (UTEP) has 3 Assistant Professor positions available in the Department of Computer Science: one in AI and two in any area of CS, including AI and Speech and Langauge Processing. UTEP has an active research group in Spoken Dialog, and new Regents Research Excellence support for a project on the Prosodic Aspects of Spanish, English and Cross-Language Communication, for which an available Research Assistant Professor position may soon be announced. Informal inquiries are welcome; please contact Professor Nigel Ward. Applications are being accepted online for the AI position and for the CS positions. Information will be shared across the searches, so there is no need to apply to both.
Stanford University, Department of Electrical Engineering
Faculty Opening
The Department of Electrical Engineering at Stanford University (https://ee.stanford.edu) invites applications for a tenure-track faculty appointment at the junior level (Assistant or untenured Associate Professor). We invite applications from individuals working at the frontiers of electrical and computer engineering, broadly defined.
An earned PhD, evidence of the ability to pursue a program of research, and a strong commitment to graduate and undergraduate teaching are required. A successful candidate will be expected to teach courses at the graduate and undergraduate levels and to build and lead a team of graduate students in PhD research.
Applications should include a cover letter, CV, 2 (two) representative publications, brief statements of research and teaching interests (3-5 pages total for both combined), and the names and e-mail addresses of 3-5 references.
Candidates should apply online.
• In the application system, within the field for “Primary Research Area for Position*” SELECT: Electrical Engineering
Applications will be accepted through December 16, 2024.
The Department Electrical Engineering, School of Engineering, and Stanford University value faculty who will help foster an inclusive academic environment for colleagues, students, and staff with a wide range of backgrounds, identities, and outlooks. Candidates may choose to include as part of their research and teaching statements a brief discussion about how their work and experience will further these ideals. Additional information about Stanford's IDEAL initiative may be found here: https://ideal.stanford.edu/
The expected base pay range for this position is:
Assistant Professor: $138,477 - $155,643
Associate Professor (untenured): $152,325 - $184,845
This base pay range is for a nine-month academic appointment and does not include summer salary. For more information about compensation and our wide range of benefits, including housing assistance, please contact the hiring department.
Stanford University has provided a pay range representing its good faith estimate of what the university reasonably expects to pay for the position. The pay offered to the selected candidate will be determined based on factors including (but not limited to) the experience and qualifications of the selected candidate including years since terminal degree, training, and field or discipline; departmental budget availability; internal equity; and external market pay for comparable jobs.
Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. Stanford also welcomes applications from others who would bring additional dimensions to the University’s research, teaching and clinical missions.
The Signal Processing Research Centre of Tampere University is looking for a tenure track-professor (assistant/associate/full) to join its team of world-class experts. Please see the full description and apply here.
** PhD Opportunities in Centre for Doctoral Training in AI for Digital Media Inclusion
** Surrey Institute for People-Centred AI at the University of Surrey, UK, and
** StoryFutures at Royal Holloway University of London, UK
** Apply by 30 May 2024, for PhD cohort starting October 2024
URL: https://www.surrey.ac.uk/artificial-intelligence/cdt
The Centre for Doctoral Training (CDT) in AI for Digital Media Inclusion combines the world-leading expertise of the Surrey Institute for People-Centred AI at the University of Surrey, a pioneer in AI technologies for the creative industries (vision, audio, language, machine learning) and StoryFutures at Royal Holloway University of London, leader in creative production and audience experience (arts, psychology, user research, creative production).
Our vision is to deliver unique cross-disciplinary training embedded in real-world challenges and creative practice, and to address the industry need for people with responsible AI, inclusive design and creative skills. The CDT challenge-led training programme will foster a responsible AI-enabled inclusive media ecosystem with industry. By partnering with 50+ organisations, our challenge-led model will be co-designed and co-delivered with the creative industry to remove significant real-world barriers to media inclusion.
The overall learning objective of the CDT training programme is that all PhD researchers gain a cross-disciplinary understanding of fundamental AI science, inclusive design and creative industry practice, together with responsible AI research and innovation leadership, to lead the creation of future AI-enabled inclusive media.
The CDT training program will select PhD students who will work on challenge areas including Intelligent personalisation of media experiences for digital inclusion, and Generative AI for digital inclusion. Example projects related to audio include:
- Audio Generative AI from visuals as an alternative to Audio Description
- Audio orchestration for neurodivergent audiences using object-based media
- AUDItory Blending for Inclusive Listening Experiences (AUDIBLE)
- Foundational models for audio (including speech, music, sound effect) to texts in the wild
- Generative AI for natural language description of audio for the deaf and hearing impaired
- Generative AI with Creative Control, Explainability, and Accessibility
- Personalised audio editing with generative models
- Personalised subtitling for readers of different abilities
- Translation of auditory distance across alternate advanced audio formats
If you have any questions about the CDT, please contact Adrian Hilton or Polly Dalton.
For more information and to apply, visit:
https://www.surrey.ac.uk/artificial-intelligence/cdt
Application deadline: 30 May 2024
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Prof Mark D Plumbley
EPSRC Fellow in AI for Sound
Professor of Signal Processing
Centre for Vision, Speech and Signal Processing
University of Surrey, Guildford, Surrey, GU2 7XH, UK
Email: m.plumbley@surrey.ac.uk
The Department of Electrical Engineering at Stanford University (https://ee.stanford.edu) invites applications for a tenure-track faculty appointment at the junior level (Assistant or untenured Associate Professor) in the broadly defined fields of electrical and computer engineering.
Applicants should have earned a Ph.D., evidence of the ability to pursue an independent program of research, a strong commitment to both graduate and undergraduate teaching, and the ability to initiate and conduct research across disciplines. A successful candidate will be expected to teach courses at the graduate and undergraduate levels, and to lead a team of graduate student researchers.
The Department of Electrical Engineering, the School of Engineering, and Stanford University value faculty who will help foster an inclusive academic environment for colleagues, students, and staff with a wide range of backgrounds, identities, and outlooks. Candidates may choose to include as part of their research and teaching statements a brief discussion about how their work and experience will further these ideals. Additional information about Stanford's IDEAL initiative may be found here: https://ideal.stanford.edu/about-ideal/diversity-statement
Applicants should submit a cover letter, CV, 2 representative publications, and names of at least 5 references. Applicants should also submit a research and teaching statement which totals 3 pages or less.
Candidates should apply online at: https://facultypositions.stanford.edu/en-us/listing/
Applications will be accepted through December 18, 2023.
Advanced driver assistance systems are key to enhancing road safety. One of the critical requirements for such systems is to reliably perceive the environment. The state-of-the-art sensors, however, are not ubiquitously deployed in driver assistance systems due to their high cost. On the other hand, simpler low-cost sensing solutions suffer from poor perception.
In this project, you will address this gap by combining sensing capacities of the human driver and the driver assistance system, to develop sensing solutions that are both affordable and reliable. Your aim will be to develop signal processing algorithms and interfaces to incorporate driver in the sensing loop of automated driver assistance systems. Your work will leverage the unique cognitive abilities of humans controlling these systems to process complex signals and make informed real-time decisions. The project will lay the foundations for understanding how human interaction with signal processing systems impacts transparency and ethical considerations in deploying hybrid human-in-the-loop solutions.
In this project, you will be able to develop your skills in designing innovative human-in-the loop sensing solutions, rapid prototyping, and evaluation of your solutions in driving simulator experiments with human participants. You will work together with Dr. Nitin Myers from the Delft Center for Systems and Control, and Dr. Arkady Zgonnikov from the Department of Cognitive Robotics at TU Delft.
The LivePerson Centre for Speech and Language offers a 3 year fully funded PhD studentship covering standard maintenance, fees and travel support, to work on cascaded deep learning structures to model speech. The Centre is connected with the Speech and Hearing (SpandH) and the Natural Language Processing (NLP) research groups in the Department of Computer Science at the University of Sheffield.
Auto-encoding is a powerful concept that allows us to compress signals and find essential representations. The concept was expanded to include context, which is usually referred to as self-supervised learning. On very large amounts of speech data this has led to very successful methods and models for representing speech data, for a wide range of downstream processes. Examples of such models are Wave2Vec or WaveLM. Use of their representations often requires fine-tuning to a specific task, with small amounts of data. When encoding speech, it is desirable to represent a range of attributes at different temporal specificity. Such attributes often reflect a hierarchy of information.
The aim in this PhD project is to explore the use of knowledge about natural hierarchies in speech in cascaded auto- and contextual encoder/decoder models. The objective is to describe a structured way to understand such hierarchies. The successful candidate is expected to propose methods to combine different kinds of supervision (auto, context, label) and build hierarchies of embeddings extractions. These propositions may have to be seen in the context of data availability and complexity. All proposals are to be implemented and tested on speech data. Experiments should be conducted on a range of speech data sets with different speech types and data set size.
The student will join a world-leading team of researchers in speech and language technology. The LivePerson Centre for Speech and Language Technology was established in 2017 with the aim to conduct research into novel methods for speech recognition and general speech processing, including end to end modelling, direct waveform modelling and new approaches to modelling of acoustics and language. It has recently extended its research remit to spoken and written dialogue. The Centre hosts several Research Associates, PhD researchers, graduate and undergraduate project students, Researchers and Engineers from LivePerson, and academic visitors. Being fully connected with SpandH brings collaboration, and access to a wide range of academic research and opportunities for collaboration inside and outside of the University. The Centre has access to extensive dedicated computing resources (GPU, large storage) and local storage of over 60TB of raw speech data.
The successful applicant will work under the supervision of Prof. Hain who is the Director of the LivePerson Centre and also Head of the SpandH research group. SpandH was and is involved in a large number of national and international projects funded by national bodies and EU sources as well as industry. Prof. Hain also leads the UKRI Centre for Doctoral Training In Speech and Language Technologies and their Applications (https://slt-cdt.ac.uk/) - a collaboration between the NLP research group and SpandH. Jointly, NLP and SpandH host more than 110 active researchers in these fields. This project will start as soon as possible.
All applications must be made directly to the University of Sheffield using the Postgraduate Online Application Form. Information on what documents are required and a link to the application form can be found here: https://www.sheffield.ac.uk/postgraduate/phd/apply/applying
On your application, please name Prof. Thomas Hain as your proposed supervisor and include the title of the studentship you wish to apply for.
Your research proposal should:
This position is fully funded by LivePerson, covering all tuition fees and a stipend at the standard UKRI rate.
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